AI RESEARCH
A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach
arXiv CS.AI
•
ArXi:2512.11944v2 Announce Type: replace-cross Motion planning for autonomous driving (AD) faces a critical trade-off. While traditional rule-based pipelines offer verifiable safety and interpretability, they often fail to generalize in complex scenarios. Conversely, emerging learning-based methods-including imitation learning (IL), reinforcement learning (RL), and generative AI-offer greater adaptability but are often constrained by opacity and safety risks.